Cracks are commonly developed in rocks (and other brittle materials), which are caused by forces and damage, and have significant effects on subsequently material strength. Meanwhile, the cracks play an important role in fluid migration (e.g. groundwater, oil and gas). Characterization or description of the cracks is the basis of the study of the relationships between mechanics, fluid mechanics and the geometry of cracks. Quantitative characterization of the cracks generally includes main parameters of the crack, such as length, width, orientation, and distribution density. For a crack network composed of intersected cracks, characterization of the cracks firstly needs to separate individual cracks from the network one by one.
Existing observations of cracks mostly use field outcrops, core samples, and also thin sections of rocks. These observations can only extract two-dimensional (2D) characteristics of cracks on the observed surface. Separation and identification of individual cracks based on 2D images or data are relatively easy to achieve because the images and data are straightforward. For example, given a 2D crack network shown in FIG. 1A and FIG. 1B, obviously, it is very easy to carry out the separation and identification of individual cracks manually. A 2D crack network with intersected and interconnected fractures, as shown in FIG. 1A, can be separated as a combination of individual cracks, and as shown in FIG. 1B, represents individual cracks after separation and identification.
When the problem is extended to three dimensions, since the distribution and intersection of the cracks in the 3D space may be extremely complex, the separation and identification one by one of the individual cracks, which the characterization of the cracks firstly needs to achieve, encounters very technical difficulties. From the complex 3D crack network shown in FIG. 2, it can be seen that the separation and identification of each of the individual cracks will be a huge challenge.
Separation and Identification of 3D Cracks
Scholars have conducted preliminary studies on identification and characterization of the 2D cracks. Dare et al. (2002) proposed a method of measuring width of an individual crack using digital images of concrete surfaces. Lee et al. (2013) proposed a method also based on digital images of concrete surfaces that automatically extracts the characteristics of the width, length, and direction of cracks, and uses a neural network to identify the cracking model. Jahanshahi and Masri (2013) also proposed a method using image thinning and distance transform techniques to complete extraction of characteristics of cracks from images. Arena et al. (2014) realized the separation and quantification of intersected cracks in a 2D image by identifying intersections. Among the domestic scholars, Yang Song et al. (2012) proposed skeleton and fractal law based image recognition algorithm for concrete crack according to the difference between the crack and other elements in fractal feature. Wang Pingrang et al. (2012) proposed recognition of cracks based on characteristics of local grids in images, which automatically calculated the trend, length and width of the crack in the recognition process.
It should be pointed out that the above studies are based on 2D images, where only one case of identification and characterization study of 3D crack can be seen. Delle Piane et al. (2015) first used the particle separation function of Avizo® software to separate the particles in the heated marble samples with complete particle morphology; and then determined an intersection of three (or more) particles as a crack junction, and partitioned the cracks that form a network using these intersections. Successful separation of cracks of this attempt is totally dependent on the enclosed form of cracks. It is noted that the conjunction point of the cracks in this method is determined by the intersection of three (or more) particles, and thus the method is only applicable to the crack system that constitutes enclosed surfaces. It is not applicable for a crack network in a general case (such as a very simple case: two intersecting cracks without reaching the boundary of the model). In addition, this method has to be operated by means of the particle partitioning function of the Avizo® software. On the one hand, the crack network formed by separation boundary obtained by the particle separation of the Avizo® software may be quite different from the crack network which can be recognized in the original image; on the other hand, the Avizo® software can handle volumes that are limited in size (Liu et al. 2016).
Characterization of Cracks
Since the development of micro-tomography (or micro-computed tomography, micro-CT), there are more studies focused on porous media, and the techniques and methods of fine characterization for pore-structures have been well developed (Ketcham & Iturrino, 2005; Nakashima & Kamiya, 2007, Liu et al., 2009, 2014).
The quantitative characterization of crack structures is difficult to develop because the separation and identification of the individual cracks fail to be achieved. Once individual cracks are separated and identified, there is no difficulty in obtaining the main parameters of the crack such as length, width, orientation, and density. Delle Piane et al. (2015), after realized the separation of the cracks with specific structure in their samples, used the statistical function of the Avizo® software to give out corresponding crack characterization and statistical results.
After the separation and identification of the cracks are achieved, each crack is recognized as a “cluster” (which has a unique label and can be understood as an identified void-structure). At this time, the characterization program ctsta10.f90 of microtomographic data may be used for crack characterization. The program was originally used for the fine characterization of pore structures, with output parameters including: porosity, specific surface area, and the size, location, shape, orientation of individual clusters (structures).
In view of this, the realization of the characterization of the cracks in a 3D space has a key technology lying in the separation and identification of the individual cracks.